[pymvpa] the effect of ROI size on classification accuracy

[Meng Liang]

Dear experts,
I got some weird results when running MVPA (to distinguish two different stimulus categories) on two fMRI datasets with different smoothing using three different ROIs. I would like to know your opinion on why this could happen.
I used linear SVM.The two datasets are from the same data acquisition but with different spatial smoothing: (1) without any spatial smoothing and (2) Gaussian smoothing with sigma=5mmThree ROIs: (A) brain area A containing 357 voxels, (B) brain area B containing 30432 voxels, and (C) brain areas A+B containing 357+30432=30789 voxels.
The classification accuracies when using dataset#1:       0.750 for A,       0.792 for B,      0.792 for A+B
The classification accuracy when using dataset#2:       0.875 for A,       0.667 for B,      0.583 for A+B
so, using unsmoothed data, combining A and B did not change the classification accuracy. However, using smoothed data, combining A and B reduced the classification accuracy considerably and the accuracy was not significantly higher than chance level any more (all other accuracies were significantly higher than chance level according to permutation test).
I would be grateful if anyone could let me know your thoughts why changing the ROI size has different effect on smoothed and unsmoothed data.
Best,Meng 		 	   		  
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